The Alcohol Use Disorders Identification Test (AUDIT): A review of graded severity algorithms and national adaptations
Bibliographic record
Abstract
Babor, T., & Robaina, K. (2016). The Alcohol Use Disorders Identification Test (AUDIT): A review of graded severity algorithms and national adaptations. The International Journal Of Alcohol And Drug Research, 5(2), 17-24. doi:http://dx.doi.org/10.7895/ijadr.v5i2.222Aims: Since it was first released in 1989, the Alcohol Use Disorders Identification Test (AUDIT) has generated a large amount of research to evaluate its psychometric properties. The purpose of this review is to critically evaluate the literature relevant to applications of the AUDIT in screening, brief intervention, and treatment referral programs, and identify national adaptations of the AUDIT to country-specific health, education, and reimbursement needs.Methods: Methods comprised a search of the world literature published since 2004, combined with review articles published since 1997.Findings: We identified 431 studies of the AUDIT, including 386 articles, 26 review papers, and 11 book chapters since 2004, with a six-fold increase in the last decade. The factor structure of the AUDIT items remains unclear, but the weight of evidence supports a two-factor model. Despite the translation of the AUDIT into numerous languages, the alcohol consumption questions were rarely adapted to suit cultural or national conditions. Although numerous studies have supported the recommended cutoff thresholds for a possible alcohol use disorder, only three studies evaluated the classification accuracy of the AUDIT’s graded severity system.Conclusions: Further development of the AUDIT score’s severity zones is needed to guide intervention selection in clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".